预算约束的双目标定向问题:GRASP_ILS

Hasnaa Rezki, B. Aghezzaf
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引用次数: 6

摘要

在本文中,我们引入了双目标定向问题(BOOP)的一个新变体,称为预算约束双目标定向问题(BOOPBC),其中对旅游者的预算施加了额外的约束。在这个问题中,有一组兴趣点(poi),每个都有两个利润和一个入场费。目标是设计参观一组景点的路线,使所参观的景点的总利润最大化,同时尊重路线的最大长度限制和游客的有限预算。据我们所知,这是第一个针对BOOPBC的研究。为了找到Pareto最优解集的近似值,我们开发了一种基于贪婪随机自适应搜索过程(GRASP)的混合元启发式算法,该算法使用迭代局部搜索(ILS)作为改进阶段。在一组从现有基准扩展而来的基准实例上对所提算法进行了测试。为了验证该方法的性能,我们还实现了一个简单的多目标抓取和路径重链接过程。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The bi-objective orienteering problem with budget constraint: GRASP_ILS
In this paper, we introduce a new variant of the bi-objective orienteering problem (BOOP) called the bi-objective orienteering problem with budget constraint (BOOPBC) where an additional constraint is imposed on the budget of the tourist. In this problem, a set of points of interest (POIs) is given each with two profits and an entrance fee. The objective is to design routes visiting a set of POIs, so that the total profits of the visited POIs are maximized, while respecting the maximum length limit of the route and the limited budget of the tourist. To the best of our knowledge, this is the first study to address the BOOPBC. To find an approximation of the set of Pareto optimal solutions, we develop a hybrid metaheuristic based on the greedy randomized adaptive search procedure (GRASP) which uses an iterated local search (ILS) as an improvement phase. The proposed algorithm is tested on a set of benchmark instances extended from existing benchmarks. We also implement a simple multi-objective GRASP with Path Relinking procedure in order to verify the performance of the proposed method.
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